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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code>.ColumnTransformer</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-compose-columntransformer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.ColumnTransformer</span></code></a></li>
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  <div class="section" id="sklearn-compose-columntransformer">
<h1><a class="reference internal" href="../classes.html#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code></a>.ColumnTransformer<a class="headerlink" href="#sklearn-compose-columntransformer" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.compose.ColumnTransformer">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.compose.</code><code class="sig-name descname">ColumnTransformer</code><span class="sig-paren">(</span><em class="sig-param">transformers</em>, <em class="sig-param">remainder='drop'</em>, <em class="sig-param">sparse_threshold=0.3</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">transformer_weights=None</em>, <em class="sig-param">verbose=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L38"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies transformers to columns of an array or pandas DataFrame.</p>
<p>This estimator allows different columns or column subsets of the input
to be transformed separately and the features generated by each transformer
will be concatenated to form a single feature space.
This is useful for heterogeneous or columnar data, to combine several
feature extraction mechanisms or transformations into a single transformer.</p>
<p>Read more in the <a class="reference internal" href="../compose.html#column-transformer"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>transformers</strong><span class="classifier">list of tuples</span></dt><dd><p>List of (name, transformer, column(s)) tuples specifying the
transformer objects to be applied to subsets of the data.</p>
<dl class="simple">
<dt>name<span class="classifier">string</span></dt><dd><p>Like in Pipeline and FeatureUnion, this allows the transformer and
its parameters to be set using <code class="docutils literal notranslate"><span class="pre">set_params</span></code> and searched in grid
search.</p>
</dd>
<dt>transformer<span class="classifier">estimator or {‘passthrough’, ‘drop’}</span></dt><dd><p>Estimator must support <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../../glossary.html#term-transform"><span class="xref std std-term">transform</span></a>.
Special-cased strings ‘drop’ and ‘passthrough’ are accepted as
well, to indicate to drop the columns or to pass them through
untransformed, respectively.</p>
</dd>
<dt>column(s)<span class="classifier">string or int, array-like of string or int, slice, boolean mask array or callable</span></dt><dd><p>Indexes the data on its second axis. Integers are interpreted as
positional columns, while strings can reference DataFrame columns
by name.  A scalar string or int should be used where
<code class="docutils literal notranslate"><span class="pre">transformer</span></code> expects X to be a 1d array-like (vector),
otherwise a 2d array will be passed to the transformer.
A callable is passed the input data <code class="docutils literal notranslate"><span class="pre">X</span></code> and can return any of the
above. To select multiple columns by name or dtype, you can use
<a class="reference internal" href="sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_column_transformer</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt><strong>remainder</strong><span class="classifier">{‘drop’, ‘passthrough’} or estimator, default ‘drop’</span></dt><dd><p>By default, only the specified columns in <code class="docutils literal notranslate"><span class="pre">transformers</span></code> are
transformed and combined in the output, and the non-specified
columns are dropped. (default of <code class="docutils literal notranslate"><span class="pre">'drop'</span></code>).
By specifying <code class="docutils literal notranslate"><span class="pre">remainder='passthrough'</span></code>, all remaining columns that
were not specified in <code class="docutils literal notranslate"><span class="pre">transformers</span></code> will be automatically passed
through. This subset of columns is concatenated with the output of
the transformers.
By setting <code class="docutils literal notranslate"><span class="pre">remainder</span></code> to be an estimator, the remaining
non-specified columns will use the <code class="docutils literal notranslate"><span class="pre">remainder</span></code> estimator. The
estimator must support <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../../glossary.html#term-transform"><span class="xref std std-term">transform</span></a>.
Note that using this feature requires that the DataFrame columns
input at <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../../glossary.html#term-transform"><span class="xref std std-term">transform</span></a> have identical order.</p>
</dd>
<dt><strong>sparse_threshold</strong><span class="classifier">float, default = 0.3</span></dt><dd><p>If the output of the different transformers contains sparse matrices,
these will be stacked as a sparse matrix if the overall density is
lower than this value. Use <code class="docutils literal notranslate"><span class="pre">sparse_threshold=0</span></code> to always return
dense.  When the transformed output consists of all dense data, the
stacked result will be dense, and this keyword will be ignored.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>Number of jobs to run in parallel.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>transformer_weights</strong><span class="classifier">dict, optional</span></dt><dd><p>Multiplicative weights for features per transformer. The output of the
transformer is multiplied by these weights. Keys are transformer names,
values the weights.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">boolean, optional(default=False)</span></dt><dd><p>If True, the time elapsed while fitting each transformer will be
printed as it is completed.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>transformers_</strong><span class="classifier">list</span></dt><dd><p>The collection of fitted transformers as tuples of
(name, fitted_transformer, column). <code class="docutils literal notranslate"><span class="pre">fitted_transformer</span></code> can be an
estimator, ‘drop’, or ‘passthrough’. In case there were no columns
selected, this will be the unfitted transformer.
If there are remaining columns, the final element is a tuple of the
form:
(‘remainder’, transformer, remaining_columns) corresponding to the
<code class="docutils literal notranslate"><span class="pre">remainder</span></code> parameter. If there are remaining columns, then
<code class="docutils literal notranslate"><span class="pre">len(transformers_)==len(transformers)+1</span></code>, otherwise
<code class="docutils literal notranslate"><span class="pre">len(transformers_)==len(transformers)</span></code>.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.compose.ColumnTransformer.named_transformers_" title="sklearn.compose.ColumnTransformer.named_transformers_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_transformers_</span></code></a><span class="classifier">Bunch object, a dictionary with attribute access</span></dt><dd><p>Access the fitted transformer by name.</p>
</dd>
<dt><strong>sparse_output_</strong><span class="classifier">boolean</span></dt><dd><p>Boolean flag indicating wether the output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> is a
sparse matrix or a dense numpy array, which depends on the output
of the individual transformers and the <code class="docutils literal notranslate"><span class="pre">sparse_threshold</span></code> keyword.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.compose.make_column_transformer</span></code></a></dt><dd><p>convenience function for combining the outputs of multiple transformer objects applied to column subsets of the original feature space.</p>
</dd>
<dt><a class="reference internal" href="sklearn.compose.make_column_selector.html#sklearn.compose.make_column_selector" title="sklearn.compose.make_column_selector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.compose.make_column_selector</span></code></a></dt><dd><p>convenience function for selecting columns based on datatype or the columns name with a regex pattern.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The order of the columns in the transformed feature matrix follows the
order of how the columns are specified in the <code class="docutils literal notranslate"><span class="pre">transformers</span></code> list.
Columns of the original feature matrix that are not specified are
dropped from the resulting transformed feature matrix, unless specified
in the <code class="docutils literal notranslate"><span class="pre">passthrough</span></code> keyword. Those columns specified with <code class="docutils literal notranslate"><span class="pre">passthrough</span></code>
are added at the right to the output of the transformers.</p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">Normalizer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ct</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
<span class="gp">... </span>    <span class="p">[(</span><span class="s2">&quot;norm1&quot;</span><span class="p">,</span> <span class="n">Normalizer</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">),</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
<span class="gp">... </span>     <span class="p">(</span><span class="s2">&quot;norm2&quot;</span><span class="p">,</span> <span class="n">Normalizer</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Normalizer scales each row of X to unit norm. A separate scaling</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># is applied for the two first and two last elements of each</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># row independently.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ct</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([[0. , 1. , 0.5, 0.5],</span>
<span class="go">       [0.5, 0.5, 0. , 1. ]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit" title="sklearn.compose.ColumnTransformer.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Fit all transformers using X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit_transform" title="sklearn.compose.ColumnTransformer.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit all transformers, transform the data and concatenate results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_feature_names" title="sklearn.compose.ColumnTransformer.get_feature_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_feature_names</span></code></a>(self)</p></td>
<td><p>Get feature names from all transformers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_params" title="sklearn.compose.ColumnTransformer.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.set_params" title="sklearn.compose.ColumnTransformer.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*kwargs)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.transform" title="sklearn.compose.ColumnTransformer.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Transform X separately by each transformer, concatenate results.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">transformers</em>, <em class="sig-param">remainder='drop'</em>, <em class="sig-param">sparse_threshold=0.3</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">transformer_weights=None</em>, <em class="sig-param">verbose=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L174"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L464"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit all transformers using X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or DataFrame of shape [n_samples, n_features]</span></dt><dd><p>Input data, of which specified subsets are used to fit the
transformers.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples, …), optional</span></dt><dd><p>Targets for supervised learning.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">ColumnTransformer</span></dt><dd><p>This estimator</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L487"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit all transformers, transform the data and concatenate results.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or DataFrame of shape [n_samples, n_features]</span></dt><dd><p>Input data, of which specified subsets are used to fit the
transformers.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples, …), optional</span></dt><dd><p>Targets for supervised learning.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_t</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, sum_n_components)</span></dt><dd><p>hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.get_feature_names">
<code class="sig-name descname">get_feature_names</code><span class="sig-paren">(</span><em class="sig-param">self</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L343"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_feature_names" title="Permalink to this definition">¶</a></dt>
<dd><p>Get feature names from all transformers.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>feature_names</strong><span class="classifier">list of strings</span></dt><dd><p>Names of the features produced by transform.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L204"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">boolean, optional</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.named_transformers_">
<em class="property">property </em><code class="sig-name descname">named_transformers_</code><a class="headerlink" href="#sklearn.compose.ColumnTransformer.named_transformers_" title="Permalink to this definition">¶</a></dt>
<dd><p>Access the fitted transformer by name.</p>
<p>Read-only attribute to access any transformer by given name.
Keys are transformer names and values are the fitted transformer
objects.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L220"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>Valid parameter keys can be listed with <code class="docutils literal notranslate"><span class="pre">get_params()</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><dl class="simple">
<dt>self</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/compose/_column_transformer.py#L542"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X separately by each transformer, concatenate results.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like or DataFrame of shape [n_samples, n_features]</span></dt><dd><p>The data to be transformed by subset.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_t</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, sum_n_components)</span></dt><dd><p>hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-compose-columntransformer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.ColumnTransformer</span></code><a class="headerlink" href="#examples-using-sklearn-compose-columntransformer" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifie..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_permutation_importance_thumb.png" src="../../_images/sphx_glr_plot_permutation_importance_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to apply different preprocessing and feature extraction pipelines ..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_column_transformer_mixed_types_thumb.png" src="../../_images/sphx_glr_plot_column_transformer_mixed_types_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py"><span class="std std-ref">Column Transformer with Mixed Types</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Datasets can often contain components of that require different feature extraction and processi..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_column_transformer_thumb.png" src="../../_images/sphx_glr_plot_column_transformer_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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